Cognitive impairments in patients with treatment resistant epilepsy: Complex rehabilitation in university clinic
Bibliographic record
Abstract
Introduction Сognitive deficit significantly affects the quality of life of patients. Aims of research was detection of cognitive impairments of varying degrees in epilepsy, and as well as studying the results of complex treatment in conditions of University clinic, physical and psychological rehabilitation, cognitive training and VNS included. Objectives We studied the features of clinical and psychopathological manifestations of cognitive impairments in patients suffering from epilepsy. Methods The study was attended by 100 patients (35 men and 65 women) who were inpatient care. The following psychodiagnostic techniques were used: the Toronto Cognitive Assessment TorCA, the test of 10 words of Luria, the MOCA test, the Münsterberg test, the quality of life scale, the Hamilton scale of depression and anxiety. Results MCI was observed in 88 % patients, dementia in 12 % (50 % - mild dementia, in 24 % - moderate dementia and in 16% - severe dementia). We used non-pharmacological rehabilitation methods for correction of cognitive impairment in epileptic patients with MCI and mild dementia during 3 mounth.. Improving of cognitive function was observed in 48 % patients, stable level of cognitive function - in 36 %, progressing of cognitive imparment - in 16 % patiens with epilepsy. Conclusions The results of the conducted research indicate the need for further study of the features of cognitive disorders in pharmacologically treatment resistant epilepsy and implementation of training aimed at improving cognitive function and preventing the progression of cognitive impairment in complex treatment of those patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".